Method of indexed storage and retrieval of multidimensional information

ABSTRACT

A tree-structured index to multidimensional data is created using naturally occurring patterns and clusters within the data which permit efficient search and retrieval strategies in a database of DNA profiles. A search engine utilizes hierarchical decomposition of the database by identifying clusters of similar DNA profiles and maps to parallel computer architecture, allowing scale up past previously feasible limits. Key benefits of the new method are logarithmic scale up and parallelization. These benefits are achieved by identification and utilization of naturally occurring patterns and clusters within stored data. The patterns and clusters enable the stored data to be partitioned into subsets of roughly equal size. The method can be applied recursively, resulting in a database tree that is balanced, meaning that all paths or branches through the tree have roughly the same length. The method achieves high performance by exploiting the natural structure of the data in a manner that maintains balanced trees. Implementation of the method maps naturally to parallel computer architectures, allowing scale up to very large databases.

This application claims the benefit of U.S. Provisional Application No.60/156,452, filed Sep. 28, 1999, which is hereby incorporated byreference in its entirety.

The U.S. Government retains certain rights in this invention due tofunding provided by contract J-FBI-98-083 awarded by the Federal Bureauof Investigation.

TECHNICAL FIELD OF THE INVENTION

The invention is related to the area of computer software for themanagement of databases. In particular it is related to the field oftree-structured indexing methods for the rapid storage and retrieval ofDNA profile information from databases containing a large number ofrecords.

BACKGROUND OF THE INVENTION

Existing database indexing methods exploit the structure inherent whenmore than one database field is used. These methods are commonly basedupon space-filling curves to map the multi-dimensional data to a singledimension, which is then indexed in the standard fashion. The B-treeindexing algorithm [1] and similar algorithms attempt to maintain abalanced index tree by adjusting the thresholds used to split theindexed parameter's value set as the tree is descended.Multi-dimensional indexing methods are found under several names, suchas R-trees [2] and R*-trees [3], and applications exist in theimplementation of image databases and other areas. A parallel databasebased upon this type of approach has been patented by IBM [4] using MPI,a widely available message-passing interface library for parallelcomputing [5]. Other implementations exist in some commercial databasesystems, such as the Informix Dynamic Server's Universal Data Option[6].

DNA profile information consists of allele information at one or moreDNA loci or sites. Typically 10 or more loci are used. Typically,individuals can exhibit either one or two alleles at each site; forensicsamples containing DNA from two or more individuals can have morealleles. The anticipated size of databases containing DNA profileinformation necessitates new methods to manage and utilize the storedinformation. An example of such a database is the national CODIS [11 ]database, which is expected to eventually store on the order of 10⁸profiles and uses complex match specifications. Standard databaseindexing structures such as B-trees, which provide rapid access torecords based upon the value of a selected database field, are not ableto take advantage of naturally occurring structure in the data. Althoughmore than one field may be indexed, the index structures are computedindependently. Retrieval of stored information based upon severalindices requires an intersection of the results of retrievals based uponeach index, which is a time-consuming operation. Methods using R-trees,R*-trees, and similar approaches rely on space filling curves ratherthan structural properties of the data. There remains a need in the artfor database structures and search engines that can rapidly andefficiently store, manage, and retrieve information from very largedatasets based upon the structural properties of the data expressed inmultiple fields.

SUMMARY OF THE INVENTION

By way of example and without limiting the application of the presentinvention, it is an object of the invention to organize the storage ofDNA profile information to minimize the time required to locate all DNAprofiles within the database that satisfy a set of user-selectedcriteria when compared against a target DNA profile and therefore matchthe target.

It is a further object of the invention to provide a method for theparallel implementation of a database of DNA profiles by breaking up thework involved in storage and retrieval of sets of information into manyrequests for work which may be distributed among a cooperating group ofcomputer hosts to balance the workload across the hosts and therebyminimize the time required to perform the work.

These and other objects of the invention are provided by one or more ofthe embodiments described below.

One embodiment is a method for performing a retrieval operation in adatabase comprising a tree of nodes. The tree of nodes comprises a rootnode which is connected to two or more branches originating at the rootnode. Each branch terminates at a node. Each node other than the rootnode may be a non-terminal node or a leaf node. Each non-terminal nodeis connected to two or more branches originating at the non-terminalnode and terminating at a node. Each leaf node comprises one or moredata records of the database. A test is associated with eachnon-terminal node that defines a partition of data records based uponeither entropy/adjacency partition assignment or data clustering usingmultivariate statistical analysis. A current node is initially set tothe root node. Input is received of a search request providing aretrieval operation and information necessary to perform the retrievaloperation. The test associated with a current node is performedresponsive to the search request. The test results in identification ofzero or more distal nodes connected to the current node. The identifieddistal nodes can, according to the test, contain the data record. Thetest is repeated using an untested distal node which is a non-terminalnode as the current node. The retrieval operation is performed on eachreferenced node that is a leaf node.

Another embodiment is a method of partitioning data records in acomputer into groups of roughly equal size. A function is defined of theprobability distribution of the values of a designated variableassociated with the data records. The function comprises a linearcombination of measures of entropy and adjacency. The values of thedesignated variable are partitioned into two or more groups such thatthe value of the function is minimized. Each data record is assigned toa group according to the value of the designated variable.

Yet another embodiment is a method of creating a tree-structured indexfor a database in a computer. The database comprises a tree of nodes.The tree of nodes comprises a root node which is connected to two ormore branches originating at the root node. Each branch terminates at anode. Each node other than the root node may be a non-terminal node or aleaf node. Each non-terminal node is connected to two or more branchesoriginating at the non-terminal node and terminating at a node. Eachleaf node comprises one or more data records of the database. Thetree-structured index comprises one or more tests associated with eachnon-terminal node. Naturally occurring sets of clusters are identifiedin the data records of the database. For each identified set ofclusters, a test is defined that assigns each data record to a clusterwithin the set of clusters. Each such test is associated with anon-terminal node, together with an associated set of clusters. Onebranch is associated with each cluster within the set of clusters. Thebranch originates at the non-terminal node and forms part of one or morepaths leading to leaf nodes comprising the data records assigned to thecluster by the test.

Still another embodiment is a method of organizing the data records of adatabase into clusters. One or more variables in each data record arerepresented in a binary form, wherein the value of each bit is assignedbased on the value of a variable. A set of variables is chosen fromthose represented in all of the data records such that principalcomponent analysis of the set of variables yields distinct clusters ofthe data records. Principal component analysis is applied to a sample ofthe data records, and two or more principal component vectors areidentified, whereby the scores of the sample data records along thesevectors form distinct clusters. A test is formulated based on theidentified principal component vectors which assigns each data record toa cluster. The test is then performed on each data record, and the datarecords are organized into clusters.

Another embodiment is a parallel data processing architecture forsearch, storage, and retrieval of data responsive to queries. Thearchitecture includes a root host processor that is responsive to clientqueries; the root host processor creates a search client object andestablishes an initial search queue for a query. The architecture alsoincludes a plurality of host processors accessible by the root hostprocessor. The root and host processors each maintain a list ofavailable host processors, query queue length, and processing capacityfor each processor. The architecture includes a bus system that couplesthe host processors and one or more memories for storing a database treecomprising nodes and data of a database accessible via the nodes. Theprocessors are capable of executing a set of tests and associate onetest with each non-terminal node of a database tree.

Yet another embodiment is another method for search, storage andretrieval of data from a database. A set of tests is defined, and onetest is associated with each non-terminal node of a database tree. Eachtest defines a partition of data of the database according to eitherentropy/adjacency partition assignment or data clustering usingmultivariable statistical analysis. A test result is output in responseto a query by evaluation of either a Boolean expression or a decisiontree.

These and other embodiments provide the art with novel, efficient, andrapid methods for the storage, retrieval, and management of largenumbers of data records using indexed databases.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 displays the relative frequency of occurrence of alleles at thed13s17 locus.

FIG. 2 presents a schematic representation of a tree-structureddatabase.

FIG. 3 shows a schematic representation of a search server residing on asingle host computer.

FIG. 4 shows a minimum entropy partition membership assignment for thed16s539 locus.

FIG. 5 shows a minimum entropy/adjacency partition membership assignmentfor the d16s539 locus.

FIG. 6 shows a minimum entropy/adjacency partition membership assignmentfor the d16s539 locus where too great a weight is afforded the adjacencycost.

FIG. 7 presents a schematic representation of the C++ Node object.

FIG. 8 provides a schematic representation of the C++ Partition object.

FIG. 9 demonstrates the effect of allele equivalence.

FIG. 10 illustrates the effect of RFLP measurement error.

FIG. 11 demonstrates search requests at loci with more than two alleles.

FIG. 12 presents a Scree plot showing the cumulative contribution madeby each principal component.

FIG. 13 shows Mahalanobis scores onto principal components 2 and 3 ofthe 10000 data profiles with alleles in d13s17 and d16s539.

FIG. 14 shows the allele frequency distribution for the d13s17 locus.

FIG. 15 shows the allele frequency distribution for the d16s539 locus.

FIG. 16 shows the joint probability density of 2-allele distribution inthe d13s17 locus.

FIG. 17 shows the joint probability density of 2-allele distribution inthe d16s539 locus.

FIG. 18 shows a joint 2-loci allele pair probability distributionpattern.

FIG. 19 shows the makeup of the second principal component for thed13s-d16s data set. Note that the allele index numbers do not correspondto the actual allele number. The tall bars correspond to alleles 11 and12 of d13s17 and alleles 11 and 12 of d16s539 respectively.

FIG. 20 shows the makeup of the third principal component for thed13s-d16s data set. Note that the allele index number do not correspondto the actual allele number. The tall bars correspond to alleles 11 (−)and 12 (+) of d13s 17 and alleles 11 (−) and 12 (+) of d16s539respectively.

FIG. 21 shows the fraction of profiles within each PCA scores clusterthat has each of the alleles in the d13s17 and d16s539 loci pair.

FIG. 22 displays a comparison of the locations of true cluster centers(*) and the approximate ones (o), which are predicted from the alleledistribution patterns at the four dominant allele positions.

FIG. 23 shows the scores plot for both the large synthetic data set andthe small real sample set. The scores from the large data set (the darkgray points) completely cover those of the small real sample set.

FIG. 24 shows the scores plot for both the large synthetic data set(medium gray points) and the small real sample set (the black darkpoints).

FIG. 25 shows a comparison of the fraction of the sample population thatis in each of the nine PCA clusters. The first of each pair of bars ateach cluster number position denotes that of the large data set (10,000synthetic Caucasian data records); the second of each pair of barsrepresents that of the small real data set (176 Caucasian data records).

FIG. 26 presents a histogram of times required to search a database of100,000 DNA profiles for an exact match to a specified profile (5,019runs).

FIG. 27 illustrates a database tree containing 100,000 DNA profiles.

FIG. 28 depicts a parallel architecture implementation of the invention.

DETAILED DESCRIPTION OF THE INVENTION

The inventors have developed a method of organizing the storage of DNAprofile information which minimizes the time required to locate all DNAprofiles within the database that satisfy a set of user-selectedcriteria when compared against a target DNA profile and therefore matchthe target. The match criteria allow for the possibility of missingallele or locus data, the inexact match of allele information at aspecified locus, an error tolerance in the number of base pairs inmatching alleles from RFLP DNA locus data, and the specification ofequivalent alleles. The match criteria can also define groups of locithat must be present in matching DNA profiles and a maximum number ofmatching profiles to be returned to the requesting user.

Definitions

A “directed graph” is a pair (N,B), where N is a finite set and B is abinary relation on N. Elements of the set N are called the nodes of thedirected graph. A “node” is an element of a set N in a directed graph ortree, such element having connections with branches that eitheroriginate from or terminate to the element. The binary relation B is aset of elements of the form (n₁,n₂), where n₁ and n₂ are elements of N.Elements (n₁,n₂) of the binary relation B are called branches or edgesof the directed graph and specify a path from node n₁ to node n₂. For adirected graph, a “path” is a set of branches {(n₁,n₂), (n₂,n₃), . . .(n_(i−1),n_(i))}, containing at least one branch that connects node n₁to node n₂ defines a path from the originating node n₁ to the terminalnode n₂. The path is said to go from node n₁ to node n₂. For a directedgraph a node n₂ is “reachable” from node n₁ if a path exists thatoriginates at node n₁ and terminates at node n₂.

A “tree” is a directed graph that satisfies two properties: (1) for anytwo nodes n₁ and n₂ a path exists from node n₁ to node n₂, or a pathexists from node n₂ to node n₁, (the graph is connected); and (2) no twonodes n₁ and n₂ exist for which paths exist from node n₁ to node n₂ andfrom node n₂ to node n₁ (the graph is acyclic). For purposes of theinvention, a tree can be either a directed graph or an undirected graph.The “root” or “root node” is the unique node of a tree that is not aterminal node for any path in the tree. A “non-terminal” or“non-terminal node” is a node of a tree that is an originating node forat least one path in the tree. A “leaf” or “leaf node” is a node of atree that is not a non-terminal node. A “subtree” of a tree (N,B) isdefined uniquely by any node n_(r) of the tree, and is the tree(N_(s),B_(s)) formed of the set N_(s) containing the node n_(r) and allnodes n∈N that are reachable from node n_(r), and the set B_(s)containing all branches that are in paths in the tree that originate atnode n_(r). Node n_(r) is the root node of the subtree (N_(s),B_(s)). Asreferred to herein, a “node” can be a carrier of information or data.

For purposes of the invention, any information or data may be optionallycontained in, referenced by, attached to, or associated with any node orbranch of a tree or directed graph. When a node has a specific structurewhich determines how information may be contained in, referenced by,attached to, or associated with the node, the node is referred to as aNode object or Node (capitalized).

Additional background information about directed graphs and trees can befound in reference 1 at pp. 86-97.

Search and Retrieval Operations in DNA Profile Databases

It is intended that the claimed invention can be used with anyappropriate database. The application of the invention to databasescontaining DNA profile information is preferred. In the description thatfollows, the CODIS system is used by example only and is not intended tolimit the scope of the invention.

Estimates of the relative frequency of occurrence of each possibleallele at each locus are known for various population subgroups. Therelative frequency distribution is typically not uniform. The currentinvention exploits this non-uniformity to improve the efficiency of DNAprofile databases. A table can be created of the known alleles that maybe present at a specific locus and their relative frequency. One suchtable for the d13s17 locus, based upon FBI CODIS data,is shown inTable 1. For this locus, there are two alleles (11 and 12) that havesignificantly larger fractions (frequencies of occurrence) than theothers. This is easily seen in FIG. 1. Non-uniform allele frequencystructure is apparent at several loci. The database search enginedescribed here exploits such non-uniformity using a “divide-and-conquer”strategy.

TABLE 1 Relative frequency of occurrence of alleles at the d13s17 locus.Bin Allele Fraction 1 7 0.000 2 8 0.115 3 9 0.078 4 10 0.070 5 11 0.3136 12 0.283 7 13 0.098 8 14 0.043 9 15 0.003 Total 1.000

A tree-structured information storage scheme is shown in FIG. 2. At eachnode of the tree, beginning at the top (root) node, a test is made uponDNA profile information (either used as a target for a search request orto be stored in the database). Based upon the test results, one or morebranches are selected that originate from the node and terminate atchild nodes where a new test is conducted. In this manner, portions ofthe database are ruled out of consideration at each level, narrowing thescope of the search. The complexity of the search method that results ison the order of log(N), where N is the number of DNA profiles stored inthe database.

For this to be effective, test results associated with the databasetree's nodes need to depend upon information at more than one locus ofthe DNA profile. In addition, the tests need to be chosen in a mannerthat causes the resulting tree to be balanced. This means that all pathsfrom the root to leaf nodes where DNA profiles are stored are roughlythe same length. This causes the portions of the database contained inthe subtrees rooted at nodes at each level of the tree to be roughly thesame size, as is shown in FIG. 2 where the percentage of DNA profiles inthe database referenced by each node of the tree is shown at that node.

The tree structure of the database has the additional benefit of beingparallelizable. Each branch leading from a node that is chosen as aresult of a test can be assigned to an independent computer host orprocessor, allowing exploration of the tree during a search to proceedon multiple computer hosts in parallel. In the illustrated database inFIG. 2, each of the three bottommost nodes can be assigned to adifferent computer, resulting in three roughly equal search problemsizes.

A unique feature of the method described here is its use of a prioriinformation about the statistical distribution of DNA profileinformation to ensure that the database tree is balanced.

Search and Retrieval Operations in Other Database Types

The multivariate statistical clustering method and information storageand retrieval methods that utilize this can be applied to other forensicscience applications. These applications include the categorization andclassification of any forensic evidence sharing one or more attributes.For example, these methods can be used to compare the events andconstruction technologies describing improvised explosive and incendiarydevices (bombs). The multivariate statistical clustering method willreveal similar cases presented to an existing bomb incident database. Inthis embodiment, clusterable variables include the presence or absenceof various types of explosives, methods of construction, and ancillarydevices such as timing devices and other triggers. This type of databaseis beneficial for determining patterns in similar bombs constructed bythe same individual(s) as well as circumstances surrounding theirplacement, the target, and the motive of the bomber. Other forensicapplications include psychological and personality profiles ofcriminals, descriptions of stolen artwork, indexing, storage, andcomparison of forged documents, linguistic content of threateningcommunications, image comparisons of spent bullet and cartridge cases,photographic images of crime scenes, determination of authorship ofcopyrighted works, and the content of computer hard drives.

Beyond forensic applications, these methods are applicable in any domainof knowledge where information to be stored, indexed, retrieved, andcompared can be characterized by the presence or absence of commonfeatures. Suitable application domains include maintenance of imagedatabases, such as arrest photos and catalogs of identifying marks(scars, marks, blemishes, and tattoos). In agriculture, image databasesare maintained of crop pests, and an important application is the rapididentification of pests on samples of infested plants. In planetaryscience, image databases are maintained of landforms and features takenfrom both space and air platforms, and the rapid identification of animage of a location on the earth's surface is important. In theseapplication domains it is possible to extract image features that can becoded by their presence or absence, allowing the utilization of themultivariate statistical clustering method and related database methods.Within each category of feature, the features may be typed by degree,such as physical size, color attributes, and texture. This typing admitsthe application of entropy/adjacency partition assignment methods as amechanism for partitioning a collection of information in order tofacilitate rapid comparison, access, and retrieval.

Another application domain is the storage of references to textualinformation. Representation of text documents by vectors indicating thepresence or absence of words and phrases, which may then provideindexing structure through the use of multivariate statisticalclustering and data access methods. Locations of words and phraseswithin a document, as well as the relative positions and frequencies ofthese words and phrases, enable the utilization of the entropy/adjacencypartitioning method and related database indexing structures. Thesetypes of representation have been utilized with singular valuedecomposition and QR factorization for text storage and retrieval [14];however, the methods described herein use clusters derived frommultivariate statistical analysis to partition the database and form adatabase tree. Wherever the application provides a natural associationof the representations of quantities such as measurements of wordpositions and frequencies with a distance or similarity measure ofassociation between data records, database trees utilizingentropy/adjacency partitions can provide highly efficient methods foridentification of records most similar to a target record referenced bya search request. In these and other applications where a binaryencoding of information relating to the presence or absence of datafeatures is appropriate, database trees utilizing clustering based uponmultivariate statistical analysis can provide highly efficient methodsto implement database indexing, search, and retrieval strategies. Inmost applications, a combination of these methods can be utilized.

The above disclosure generally describes the present invention. A morecomplete understanding can be obtained by reference to the followingspecific examples, which are provided herein for purposes ofillustration only and are not intended to limit the scope of theinvention.

EXAMPLE 1 Database Implementation

A key to implementation of the search specification on a tree-structureddatabase is what occurs at the nodes of the database tree. These nodescan be C++ objects and can contain partition objects used to describehow the database is segmented at each node. Two types of partitioning atthe nodes are illustrated: entropy-adjacency partition assignment anddata clustering using multivariate statistical analysis. The database isimplemented using a Search Queue and one or more Search Engines in eachcomputer host in a single or parallel computer environment. The SearchQueue holds Search Requests and additional information such as requeststo store or delete DNA profile information in the database. The SearchEngines take elements from the Search Queues and perform the requestedactivities. During this process, additional Search Requests may begenerated, which each Search Engine places in the Search Queue. TheCODIS search engine communicates with clients that request serviceacross a network interface, and returns the requested information tothese clients. This process is shown schematically in FIG. 3 for thesingle host case. Multiple hosts in a parallel computing environment areaccommodated using multiple communicating copies of this process. Thehosts can either all operate on the same database, or each can contain aportion of the database; a mixture of the two methods can also be used.As an example, communicating groups of processors may operate where allmembers of each group are assigned the same portion of the database.

In FIG. 3, the client initiates a request for service with the Server,which is a computer process whose sole function is to broker exchangesbetween clients and the CODIS search server. Upon receipt of aconnection request, the Server instantiates a Server Client, which is acomputer process or thread dedicated to servicing the client. If theclient is initiating a request to search the database, informationnecessary to define the Search Request is transmitted from the Client tothe Server Client, and the Server Client assembles a Search Request andinserts it in the Search Queue (labeled “Queue of Search Requests” inFIG. 3).

A Search Engine removes the topmost (oldest) Search Request from theSearch Queue when it becomes available. The Search Request specifies anidentifier for the requesting Client, an associated node of the databasetree at which the search is to begin, and a set of target DNA profilesand related information specifying the context of the search. If thedatabase tree node is not a leaf node (has descendents), the SearchEngine can use one of the available partitioning methods to determinewhich nodes at the next lower level of its database tree must besearched. If the node is a leaf node, the Search Engine searches the setof DNA profiles stored at the node for matches. This process may eithergenerate additional Search Requests or matching DNA profiles. SearchRequests are placed on the Search Queue, and matching DNA profiles arereturned to the Client. The Search Engine can follow one branch to anext lower node and repeat the process in order to achieve higherperformance rather than insert the corresponding Search Request onto theSearch Queue. The Search Engine block in FIG. 3 is schematically shownas a single process or thread, but it should be understood to representone or more Search Engines on a single computer host.

Various methods can be utilized to balance the loads of the computerhosts so that the average waiting times for service and computation inSearch Queues and Search Engines across all hosts are equalized. Forexample, blocks of Search Requests can be exchanged among hosts fromhosts with relatively long average waiting times to hosts with shorterwaiting times. A stochastic scheduling method can be utilized, causinghosts with relatively short waiting times to, on average, receive moreexchanges than hosts with longer average waiting times. The sizes of theblocks exchanged can be adjusted to accommodate the relative speeds ofthe processors and the inter-processor communications protocols andhardware. Either of two software packages used for parallel computing,MPI [5] and PVM [7], or other similar packages, can be used to implementthe balancing method.

The Main block shown in FIG. 3 starts the execution of the Server,Search Queue, and Search Engines on a computer host and initialized theenvironment to allow these processes to communicate with other hostsparticipating in the parallel computer environment. In addition, variouslog files can be generated to aid in debugging problems and tracingutilization of the CODIS search server; two of these are shown in thefigure.

EXAMPLE 2 Entropy-Adjacency Partition Assignment

To minimize worst-case search time, division of the database into Nroughly equal portions at each level of the database tree is highlydesirable. A simple and fast test is needed to accomplish this. One testmethod that can be used to accomplish this is entropy-adjacencypartition assignment. This method assigns members of the set of possibleallele pairs at a specified locus to groups. The goal is to choose thesegroups so that their expected sizes are roughly equal, and so thatalleles with indices that differ by a small number (corresponding to thenumber of repeated sequences for STR DNA profiles and the number of basepairs for RFLP DNA profiles) have a high probability of being assignedto the same group. By preferentially assigning alleles differing by asmall number of base pairs to the same group, the growth of the numberof generated search requests due to a client's specification ofequivalent alleles will be less than would be the case for otherassignments.

The set of possible allele pairs at a locus can be viewed as atwo-dimensional grid, where each row or column corresponds to an allele.Since the pair (allele1, allele2) is the same as the pair (allele2,allele1), any partition assignment on this grid is symmetric. Thus, onlyhalf need be shown. A partition assignment that minimizes the entropy ofthe resulting partitions is shown in FIG. 4 for the d16s539 locus. Inthe figure, different shadings of gray (colors can also be used)correspond to different partition membership assignments. The axes arelabeled with the sequence numbers of the d16s539 alleles; the allelesrange from 5 to 15 inclusive. Entropy is a concept from informationtheory, and for the partition assignment problem minimum entropy isequivalent to creating a partition whose members are as close aspossible to the same size (expected number of elements). From thefigure, it is apparent that minimum entropy assignment does not tend toassign adjacent allele pairs to the same partition member.

The partition assignment problem can be solved by a global optimizationprocedure based upon simulated annealing. In this method, an initialrandom assignment is chosen, and its cost (entropy) is calculated. Theassignments are represented by non-negative integers. In the figure, adivision of the allele pairs into six partition members is desired, andthe members are labeled with the integers 0 through 5, inclusive. Theoptimization procedure randomly chooses an allele pair to modify andrandomly chooses a proposed new assignment for that pair. The change incost that would result if the new assignment were used is calculated,and if the cost decreases the proposed change is accepted. If the costincreases the proposed change is accepted with a probability p thatbegins with unity and declines monotonically with iteration number asthe optimization process proceeds. An exponentially decreasingprobability of acceptance (a geometric sequence) has been found to workwell in practice. The optimization procedure terminates when either thecost has not been further decreased over a specified number ofiterations or a maximum number of iterations has been achieved. The lastcomputed assignment is used as the solution to the problem. A variationof this procedure, which is used in the Examples, is to maintain a copyof the best (lowest cost) assignment achieved through the currentiteration, updating this as better assignments are found, and to use thelast best assignment as the optimal assignment.

Preferential assignment of adjoining allele pairs can be achieved byintroducing a cost associated with the absence of adjacency. For everyallele pair (A), the four (less on the boundaries) assignments forallele pairs that differ by one index in one allele are examined, and acount variable is initialized to zero. For every assignment that differsfrom the assignment of allele pair (A), a one is added to the countvariable. The count variable is then scaled by the probability of theallele pair's occurrence, and these scaled values are summed over allpossible allele pairs to form the adjacency cost. An allele pair withzero probability of occurrence can allow the assignment of that pair tobe arbitrarily made without regard to adjacency. To avoid this problem,a small number can be added to the probabilities of occurrence, or tothose that are zero, causing the assignment to affect the cost. Theresults reported herein utilized a value of 0.005 added to all allelepair probabilities of occurrence. The resulting adjacency cost islinearly combined with the entropy cost, and the combined cost isminimized using the global optimization procedure. This can be expressedby the equation

Cost=Entropy+Weight*Adjacency

where Entropy is the cost due to the non-uniform size of the partitionmembers, Adjacency is the cost due to the existence of adjacent allelepairs having different assignments, and Weight is a non-negative numberdefining the relative importance assigned to the adjacency cost.

For certain linear combinations, this cost function results in adjacentgroups of allele pairs being assigned to the same partition memberwithout drastically impacting the entropy (measure of non-uniformpartitioning) of the result. This effect can be seen visually in FIG. 5,where the allele pairs of locus d15s539 are partitioned into six groups.For this partition assignment the entropy is 1.01201, whereas for theassignment shown in FIG. 4 the entropy is 1.0111.

This process can be carried to an extreme by weighting the adjacencycost too heavily. In this case, the number of partition membersdecreases with some members containing zero elements. This effect isvisible in FIG. 6. At the present time a precise way to select the“best” trade-off between entropy and adjacency is not known. Ifminimization of entropy cost is too heavily favored, search performanceusing equivalence and RFLP error tolerances will be adversely affected.If adjacency is too heavily favored, the database tree will be becomeunbalanced, resulting in “long legs” and poor worst-case performance.“Engineering judgment” can be used to select a partition map (via theweight) from many computed solutions that will yield acceptableperformance. This can be done by computing optimal solutions to theassignment problem for a variety of non-negative weights. If the weightis too large not all partitions will contain assigned allele pairs. Ifthe weight is too small assignments similar to those shown in FIG. 4will be observed. Iteration may be required to determine suitablevalues.

EXAMPLE 3 Database Design Using Entropy-Adjacency Partitions

A schematic representation of the database tree was presented in FIG. 2.In that figure, each node of the tree is represented as beingimplemented using an entropy-adjacency partition. In practice, this isonly one of two methods that may be used at a node, and the tree maycontain a mixture of the two cases. The implementation of the tree nodesusing entropy-adjacency partitions will be discussed in detail in thissection; however, the implementation of the tree nodes can also beaccomplished using data clustering.

A decision tree node can be implemented by a C++ Node object, as shownschematically in FIG. 7. The object can contain a unique identifyinginteger stored in the thisnode parameter. A Node object may be either aleaf or non-leaf (non-terminal) tree node, as specified by the Node dataelement isleaf. If it is a leaf, the node can store DNA profileinformation located at that portion of the tree in a storage datastructure. As DNA profiles are being stored into the database, athreshold is utilized to determine at what point a leaf node should beconverted to a non-terminal node, resulting in two or more nodes onelevel below the node. Nodes can track the total number of DNA profilesthey reference in the nmembers parameter. The offset parameter can beused when stored DNA profiles are located out of processor memory, forexample on a disk drive to locate the information relative to the startof the storage media.

Non-terminal Node objects can maintain a list of nodes (nextnode),referenced by the locations of the corresponding Node objects in anarray, that can be reached by branches from the nodes and are one levelbelow the nodes in the database tree. Nodes based upon entropy-adjacencypartitions can contain a pointer to a C++ Partition object (ppart),which can implement the data structures and methods necessary to utilizethe partition information. For each allele pair associated with apartition, a count of DNA profiles matching that allele pair can bemaintained by the Node object in the allele cnt map. This informationcan be utilized to avoid searches along branches from the node thatcontain no DNA profiles capable of matching a target DNA profile.

A C++ Partition object can be used to store entropy-adjacency partitionassignment information. A Partition object defines which subset of thedatabase associated with the database tree node a profile belongs in.These objects are represented schematically in FIG. 8. A stringidentifying the locus used for the partition can be stored in a nameentry. A nmembers entry can specify the number of groups in thepartition. Missing allele pair values can be accommodated; a probabilityof the occurrence of missing data can be maintained in a pmissing entry.A vector of probabilities of occurrence, one for each possible allele,can be maintained in a popstat structure. The table of partitionassignments, along with the probabilities of occurrence of each allelepair, can be maintained in a table map. Each entry of this map is aPartEntry object containing the assignment and probability.

Partitions can be used by many database tree nodes and are therefore notusually included within the Node objects. Rather, a pointer in the Nodeobjects can be maintained to the appropriate Partition object. The Nodescan be stored in an array in a predetermined order which is consistentacross all hosts participating in the parallel machine, allowing SearchRequests to be exchanged across host boundaries.

EXAMPLE 4 Mapping Match Specifications to Methods

The CODIS system provides detailed specifications [11] governing how twoDNA profiles may match. A matching algorithm used with CODIS mustcorrectly account for:

PCR allele equivalence,

RFLP measurement error,

Match stringency on a per-locus and per-profile basis,

Mismatch on a maximum allowed number of loci,

Matches on a minimum number of loci,

Completeness, and

The maximum number of DNA profiles to be returned in response to asearch request.

Most of these specifications can be interpreted using locus partitionand search state information.

Search Requests can be evaluated by the C++ Node objects. The node canuse the Search Request's stored information, along with the partitioninformation referenced by the Node, to generate results, which arereturned to the requesting Client, or new Search Requests. Results areonly generated when the Node object is a leaf node and contains DNAprofiles. If PCR alleles at a locus are declared equivalent, then a DNAprofile target provided by a Search Request that contains one of thesealleles must match all of the equivalent alleles as well. This is shownin FIG. 9 where the Search Request contains a target profile with alleleinformation for locus d13s17 (alleles 3 and 5), and declares thatalleles 2 and 4 are equivalent to allele 3. The yellow (lighter) “X” inthe figure corresponds to the allele pair (3,5) and is located in thepartition assignment designated by the blue shading. The pink (darker)“X”'s in the figure correspond to the allele pairs (2,5) and (4,5), bothof which also match (3,5) because of the declared equivalence. Since theallele pair (4,5) is assigned the partition designated by the yellow(light) shading, two new Search Requests are generated (assuming theallele_cnt table entries are positive) for the “blue” (dark) and“yellow” (light) partitions.

The search behavior induced by RFLP measurement error is similar to thecase of PCR allele equivalence. Measurement error in CODIS isrepresented by percent relative error bounds on the stored values ofRFLP bands. The result is a region (represented by a square in the nextfigure) within which a band is required to match RFLP DNA targetinformation. Any squares representing ranges of stored RFLP data thatintersect this region can cause new Search Requests to be generated, asshown in FIG. 10.

DNA profile loci can contain more than two alleles due to the presenceof mixed samples (DNA material from multiple individuals). In this caseall pairs that can be formed from the alleles that exist at a locus areused to reference partition cells. In FIG. 11 the DNA profile targetcontains the alleles 3, 4, and 6 at the d13s17 locus. As a result, theallele pairs (3,4), (3,6), and (4,6) are used to determine new SearchRequests.

CODIS defines the concept of match stringency. High stringency matchesrequire the presence of exactly the same alleles at the locus in thetarget and retrieved samples. Medium stringency allows additionalalleles to be present in the retrieved samples and some loci, and lowstringency allows a partial correspondence between the alleles totrigger a match. Work on implementation of the medium and low stringencymatch methods is in progress; however, conceptually these cases are verysimilar to what is required for equivalent alleles and RFLP errorbounds.

In order to discuss how thresholds on the maximum number of allowedmisses and the minimum number of required matches are handled, it isnecessary to describe a representation of Search Request objects. Missesdue to the absence of information for a locus can be handled in asimilar fashion. These thresholds affect the number of Node objects inthe tree that must be evaluated and can lower search performance if theyare chosen poorly. A Search Request object can maintain the followinginformation:

node: the node number in the database tree where the search is to occur(initially zero, indicating the root node)

pPartPrfl: a pointer to the search information (partition profile)

pResult: a pointer to a place to put search results

mismatch: a count-down counter of mismatches allowed.

The mismatch counter is an example of the Search Request object'sability to carry search state information. This counter specifies thenumber of misses that may be accumulated from the current point on inthe search. Every time a miss is allowed at a node the mismatch counteris decremented and stored in the new Search Request object.

A missing allele is equivalent to the homozygous case. Missing locusdata can be handled using either of two approaches. The first uses aspecial partition entry to reference profiles with no information forthe locus. The second stores profiles with missing alleles in allpartition entries of the partition corresponding to the locus whichwould be capable of matching the profile if an allele were present. Thefirst method increases the size of the database tree; the second methodincreases the number of nodes that must be searched. Because the secondmethod essentially removes the ability to avoid searching partitionshaving no entries, the first method is preferred.

A constraint can be placed on the maximum number of matching DNAprofiles. Search Queue objects can provide the Search Engines with anindication that the maximum number of targets has been returned for aspecific search request, causing the Search Engines to ignore subsequentsearch requests with the same identifier. The Search Queue objectsreceive this notification from the Server Client, which receivesmatching DNA profiles as they are generated.

The CODIS completeness condition is fairly complex, requiring thedetermination that specific combinations of loci data are present inmatching DNA profiles. This condition is evaluated only at leaf nodes ofthe database tree to exclude profiles that fail the requirement.

EXAMPLE 5 Multivariate Statistical Clustering Method

This section provides a description of a method that uses multivariatestatistical methods to determine clusters that can be utilized topartition portions of a database into groups of roughly equal size. As aresult, this method generates partition information that can beincorporated within or associated with an arbitrary Node object in adatabase tree. The application of this method to DNA profile data basedupon amplification of short tandem repeat (STR) DNA locus data ispresented. For this case, a clear binary encoding of the alleles presentat a STR locus is available. For other data types, such as DNA RFLPallele (band) data, the proper choice of a binary encoding scheme is notas easy to determine, and at the present time the binary encoding isnecessary.

The DNA STR profiles in the database are first represented in a binaryform, using a ‘1’ to denote the presence, and a ‘0’ to denote theabsence of an allele at a locus. Based on the allele distributionpatterns among two chosen STR loci, clusterable patterns are discernableafter principal component analysis of the data matrix. Distinctclusters, usually less than 10, can be established using a clusteringmethod, such as k-means [12]. The membership of each cluster is thenidentified and recorded. Each DNA STR profile belongs to one and onlyone of these clusters. Thus, the entire set of DNA profiles in the database can be partitioned into these clusters, based on the alleledistribution at these two chosen loci.

When searching for matching profiles to a target profile, the target'sDNA profile can be classified into one of these clusters, based on itsallele distribution information at these two loci. Thus, a subsequentsearch can be restricted to members within this cluster. This reducesthe search problem by approximately one order of magnitude.

The search process continues by examination of the target's alleledistribution at other pairs of STR loci, resulting in classification toa subsequent PCA cluster and reduction of the number of possible matchesby another order of magnitude at each level of the database tree.Partitions based on PCA clustering can be inserted into a nonterminalNode object of the database tree at any point and freely intermixed withpartitions based upon entropy/adjacency partition assignment.

EXAMPLE 6 Binary Representation of the STR Profile

The STR profiles are first converted into a binary format, with a ‘1’representing the presence and a ‘0’ the absence of an allele at eachlocus. Thus the binary representation of a collection of DNA STRprofiles is a sparse matrix of mostly zeros and some ones. Each row ofthis matrix is the representation of one DNA STR profile. The maximumnumber of 1's for each profile (for samples that are not mixtures ofmaterial from two or more individuals) is two times the number of loci,assuming heterozygous presence of alleles at each locus. The minimumnumber of '1's is equal to the number of loci used for each profile,assuming homozygosity at all loci.

EXAMPLE 7 Clustering by Principal Component Analysis

Principal component analysis (PCA), a popular method of performingmultivariate statistical analysis, represents a matrix of highdimension, consisting of correlated information, with a much lowerdimensional matrix, without sacrificing significant informationcontained in the original data matrix. PCA involves a rotation from theoriginal frame of reference to a new frame of reference, whose axes aregiven by the principal components from the PCA. The first principalcomponent represents the direction along which the variance exhibited bythe original data points is maximized. The second principal component,orthogonal to the first, represents the direction along which theremaining variance is maximized. Additional principal components aredefined in a similar fashion.

EXAMPLE 8 PCA by Singular Value Decomposition

To implement PCA, the preferred method is to use the singular valuedecomposition (SVD) [9] to decompose the data matrix, X, into theproduct of three matrices, in which the columns of the matrix, V, arereferred to as the “principal components” of the PCA of the data matrix,X. Other methods known in the art may be used to obtain equivalentinformation. Thus,

 X=UΣV ^(T)  (Eq. 1)

where U and V are orthogonal matrices, and Σ is a diagonal matrix withpositive elements arranged in descending order. The columns of V, beingthe principal components, represent the coordinates or basis of the axesof the new frame of reference. The ratio of the square of each singularvalue to the total sum of squares of all the singular values representsthe percentage of the total variation contributed by each principalcomponent. A Scree plot can be developed to show the cumulative ratio ofthis measure; an example is shown in FIG. 12. Since the original dataare assumed to be heavily correlated, and the singular values arearranged in descending order, one can make a decision as to how manyprincipal components to keep in building the PCA model to represent theoriginal data. The discarded data along the remaining principalcomponents are regarded as less important and are ignored.

EXAMPLE 9 The Principal Components

Each principal component is of unit length and orthogonal to all otherprincipal components. The principal components are the columns of theright singular matrix, V, of the SVD of the data matrix, X, above. Eachprincipal component is expressed as a linear combination of the originalvariables, with the entries of the principal component expressing thatparticular linear combination. The absolute values of all entries areless than or at most equal to 1. Therefore, those entries withrelatively large values indicate the heavier weight their correspondingoriginal variables occupy in making up this particular principalcomponent. The variables with correspondingly heavy weights are also theones being correlated in the original data set. If the columns of thedata matrix X are not first mean centered, such that the mean of eachtreated column is zero, then the first principal component reflects theaverage values of the variables represented in the new principalcomponent frame of reference. It is then the next few principalcomponents that serve to differentiate between profiles. Therefore, meancentering is an optional step which provides no additional capabilityand is not performed here.

EXAMPLE 10 The Scores Vectors and Normalized Scores Vectors

After the principal components are found, each data profile can beprojected onto each principal component. The projected vector isreferred to as the scores vector. The length of the scores vectorindicates how closely aligned that data profile is to that principalcomponent. The bigger the projection, the better this principalcomponent represents this data profile. Thus, data profiles withcomparable projections onto this principal component can be regarded as“similar” to each other, with respect to this principal component. Thoseprofiles with high projected values onto this principal componentindicate that these profiles are highly aligned with that of theprincipal component, therefore representing more of the originalvariables which are heavily weighted in that principal component.Similarly, projections of data profiles onto each of the succeedingprincipal components can be carried out to get the scores and theirprojections onto those principal components.

Because of the different degree of variation exhibited by the dataprofiles along the different principal components, normalization isnecessary, such that normalized distances from the origin to eachprojected point can be compared meaningfully to each other. Therefore,the Mahalanobis distance measure [12] is employed, in which eachprojection is divided by the corresponding singular value. TheMahalanobis distance scores are calculated as follows:

Mahalanobis_scores=XVΣ⁻¹=(UΣV′)VΣ ⁻¹ =U  (Eq. 2)

where X represents the original data matrix, and U, Σ and V are from theSVD of X, as shown in Eq. 1. Postmultiplying X by V performs theprojection of the rows of X (profiles) onto the principal components,with the projected vectors represented with respect to the principalcomponent axes. Postmultiplying XV by Σ⁻¹ scales each column of XV bythe corresponding singular values contained in Σ. A two dimensional plotcan be used to show the scores onto principal components PC2 and PC3. Inplotting the scores plot in, say PC2 and PC3, it is the row entries fromthe second and the third columns of the Mahalanobis_scores matrix (the Umatrix in Eq. 2) that are plotted in a 2-d plot. From henceforth, theMahalanobis_scores shall simply be referred to as the scores. An exampleof such plot is shown in FIG. 13, which shows the scores for 10000 DNASTR profiles in the ds13s17 and d16s539 loci onto the 2nd and 3rdprincipal components. It is in such a scores plot that clusterability ofthe sample points is examined.

EXAMPLE 11 Identification of Clusters

A clustering algorithm can be employed to perform clustering of thescores projected onto a 2-d principal component space. K-means [12] isselected because of its wide use and simplicity. However, with k-meansthe number of clusters has to be specified before the algorithm canbegin. This is not a problem because the choice of the two loci, the twoprincipal components on which to project the data, as well as the numberof clusters associated with the scores, are all identified by a priorivisual inspection and recorded.

K-means clustering starts with an arbitrary set of n points, where ndenotes the number of desired clusters, to serve as the center of eachcluster. Each data point is assigned to that cluster to which it is“closest” using a distance measure of the user's choice. The standardEuclidian distance measure is used here. This is followed by acalculation for the new center points of the resultant n clusters. Then,in the next round of iteration, clusters are re-formed by assigning toeach of the new centers points that are now closest to each. Iterationscontinue the cluster centers no longer change or a specified number ofiterations is reached.

After clusters are formed, the membership of each cluster can beidentified and the corresponding DNA STR profiles can be extracted fromthe original database for future study.

EXAMPLE 12 Projection of New DNA STR Data Set onto the PrincipalComponents of Another

The DNA STR profiles of one group can be compared to that of another bycomparing the corresponding scores patterns onto a principal componentreference frame. To do the comparison, the projections of the profilesof the second set may be normalized by the inverse of the singularvalues of the first set. The projection and the normalization to arriveat the Mahalanobis scores of the second data set is carried out asfollows:

M_scores_(2nd) =X _(2nd) V _(1st)Σ_(1st) ⁻¹  (Eq. 3)

M_scores denotes the Mahalanobis scores, which shall simply be referredto as the scores. In plotting the scores plot in, say PC2 and PC3, it isthe row entries from the second and the third columns of the M_scoresmatrix that are plotted in a 2-d plot.

EXAMPLE 13 Principal Component Analysis of the Synthetic Data in 2 Loci

Study of clustering by PCA was carried out with two sets of data. Thefirst was a synthetic data set, generated from the known allelefrequency distribution for each of sixteen STR loci. The distribution isfrom the CODIS data base. A binary set composed of 10,000 profiles withallele specifications at 16 loci was thus generated. This data matrixhas the dimension of 10,000 by 202, and is sparse with all entrieseither 1 or 0. Each row denotes the STR profile in all 16 loci for oneindividual; each element of a column represents the presence (1) orabsence (0) of the corresponding allele in the each of the 10,000individuals. The second set of data studied was compiled from humanpopulation studies and released by B. Budowle [10] of the FBI, and iscomposed of DNA STR information of six ethnic groups with about twohundred samples in each group. A PCA model was developed with the largesynthetic data set, and the small real data set was projected onto theprincipal components derived from the former. Relative percentages ofmembership profiles in the clusters were also compared between the largeand the small data sets in order to compare the corresponding allelefrequency distributions.

The locus pair of d13s17 and d16s539 was chosen for illustration of thePCA analysis and clustering study. The columns corresponding to thealleles of these two loci from the 10,000 by 202 synthetic data set wereextracted, and subjected to singular value decomposition (SVD) to obtainthe principal components (the columns of the V matrix in Eq. 1) and theMahalanobis scores vectors (the columns of the U matrix in Eq. 1). Thecorresponding columns of the data matrix, X, extracted are columns 11through 19 (corresponding to alleles 7-15 of the d13s17 locus) andcolumns 20 to 30 (corresponding to alleles 5-15 of the d16s539 locus)for a total of 20 columns. The SVD of this submatrix of size 10,000 by20 was computed. First, the number of principal components to retain tobuild the PCA model was ascertained. FIG. 12 is the Scree plot showingthe cumulative contribution made by the principal components. The plotshows that the first three principal components together capture about60% of the total variation exhibited by the original data matrix. Itfurther shows that the rank of the matrix is 14, meaning there are only14 independent columns among the total of 20 columns of X. Note thateach successive principal component contributes less to the overall datavariation, as foreshadowed by the decreasing magnitude of eachsuccessive singular value squared of the data matrix, X.

EXAMPLE 14 Scores Plot onto PC2 and PC3

The 10,000 profiles with alleles at d13s17 and d16s539 were projectedonto the second and third principal components, followed bynormalization by the inverse of the corresponding singular values toarrive at the Mahalanobis scores. The entries of each row afterprojection and normalization were plotted in a 2-d scores plot. FIG. 13shows the result. Nine distinct clusters were observed.

EXAMPLE 15 Analyzing the Clusterability of Other 2-Loci Combinations

The clusterability of other 2-loci combinations was also studied. Thereare a total of 16 loci available for analysis. Therefore, a total of 1202-loci combinations (16*15/2=120) were analyzed. Table 2 shows those2-loci combinations and the corresponding supporting principalcomponents that yield good and distinct clusters. The reason that onlycertain 2 loci combinations yield good clusters is further analyzed soas to understand the role the alleles at each locus play in determiningthe clusterability of the profiles. The following subsections presentthe rationale. Briefly, however, those loci pairs with alleleprobability densities concentrating at just a few of alleles tend toyield good and distinct clusters.

TABLE 2 Combinations of two loci that give good clusters. Thecorresponding principal components are shown, as well as the number ofclusters. V1 and V2 denote the identity of the first and the secondprincipal component specification, onto which good clustering of theprojected scores is observed. Locus 1 Locus 2 V1 V2 N clusters csf1pofga 2 3 7 csf1po tpox 2 3 7 d13s17 d16s539 2 3 9 d13s17 d1s80 2 3 9d13s17 d21s11 2 3 9 d13s17 d5s818 2 3 9 d13s17 d7s820 2 3 9 d13s17 fga 34 7 d13s17 hol 2 3 9 d13s17 vwa 2 3 9 d16s539 d1s80 2 3 9 d16s539 d5s8182 3 9 d16s539 d7s820 2 3 9 d16s539 fga 4 5 7 d16s539 tho 1 2 3 9 d16s539vwa 2 3 9 d18s51 d1s80 4 5 7 d18s51 d21s11 2 3 7 d18s51 d3s1358 2 3 7d18s51 d5s818 2 3 7 d18s51 d8s1179 2 4 7 d18s51 fga 2 3 7 d18s51 tpox 45 7 d18s51 vwa 2 3 7 d1s80 fga 3 4 7 d21s11 d5s818 2 3 9 d21s11 d7s820 23 9 d21s11 fga 2 5 9 d21s11 tho 1 2 3 9 d21s11 vwa 2 3 9 d3s1358 fga 2 37 d3s1358 tpox 2 3 7 d5s818 d7s820 2 3 9 d5s818 tho 1 2 3 9 d5s818 vwa 34 7 d7s820 d1s80 2 3 9 d7s820 d8s1179 2 3 9 d7s820 vwa 2 4 7 d8s1179 fga3 4 7 vwa fga 2 3 7

EXAMPLE 16 Cluster Formation

The allele frequency distributions for the 2-loci combinations thatyielded good clusters were examined to discover the reason behind theirclusterability. It was found that those loci with allele probabilityconcentrated at just a few alleles (2 to 4) are good candidates to givegood clusters. The main reason is that with just a few alleles possible,the joint 2-loci allele distribution tends to concentrate in thoseallele pairs with relatively high probability of occurrence. Thus lessbut more distinct clusters tend to be formed. FIGS. 14 and 15 show therelative frequency of occurrence of the alleles at the d13s17 andd16s539 locus, respectively. Notice that alleles 11 and 12 in both locihave a much higher probability of occurrence. FIGS. 16 and 17 show thejoint 2-allele frequency distribution for the d13s17 and d16s539 locusrespectively. It is noted that only a few of the allele pairs haverelatively high probability of occurring. This distribution pattern isto be contrasted with one where the majority of the allele have someprobability of occurring but none is much higher than others. FIG. 18shows the joint 2-loci allele-pair probability density for the d13s16and d16s539 loci. Again, it is observed that most probability densitiesare concentrated at a few selected allele pairs, corresponding to thosealleles with relatively high probability of occurring within each locus.

EXAMPLE 17 Interpreting SVD of Data Exhibiting Good Clusterability

Consider the allele distribution patterns in a large DNA STR data set.If for a specific locus, the probability densities concentrate in only afew, for example 3 out of 10, alleles, then the majority of the profilesin this data set will have alleles for that locus, corresponding tothose with high probability densities. However, some, though in theminority, will still have alleles with low probability densities. Thus,the variance among the profiles associated with this locus will behigher than those where a large number of alleles have comparable butlow probability densities. The large variance exhibited by this part ofthe data will be picked up by the leading principal components of theoriginal data matrix. Recall that the principal components of a matrix Xare given by the right singular vectors of X, after SVD (the columns ofthe matrix, V, from Eq. 1). For a matrix without any column meancentering, the first principal component generally gives just theaverage of the overall data, and therefore is not useful indifferentiating between the points. The second principal component,therefore, is the one that gives the direction along which the varianceexhibited by the original data matrix is maximum; the third principalcomponent gives the direction that captures the next maximum variance,after the component along the first and second principal component havebeen subtracted off from the original data matrix.

As a result of the above reasoning, the first few leading principalcomponents after the first, should be contributed heavily by thoseoriginal variables (i.e., the alleles) that have the concentrated alleleprobability densities. FIGS. 19 and 20 show the make up of the secondand the third principal components of the 10,000 profiles at the d13s17and the d16s539 loci.

EXAMPLE 18 Interpreting the Principal Components

It is clear from the FIGS. 19 and 20 that the most significant allelesin principal component 2 are alleles 11 and 12 of d13s17, and the mostsignificant for principal component 3 are alleles 11 and 12 of d16s539.Alleles 11 and 12 of d16s539 also contribute some to PC2, and alleles 11and 12 of d13s17 also contribute some to PC3. Notice the opposite signsof alleles 11 and 12 of each locus in each PC. What this means is that,if a cluster of the scores of the DNA profiles projects highly onto thepositive direction of PC2, then most members within this cluster havethe presence of allele 12 (the second tall bar of FIG. 19) of d13s17,the presence of allele 11 of d16s539 (the first tall bar of d16s539 ofFIG. 19), the absence of allele 11 in the first locus, and the absenceof allele 12 in the second locus, respectively, since the signsassociated with the latter pair are negative.

It was observed that cluster 9 from the scores plot of FIG. 13 projectshighly along the positive direction of PC2. In FIG. 21 it is evidentthat in cluster 9, “all” of the members have allele 12 of the d13s17locus, as well as allele 11 of the d16s539 locus. Further, none of theprofiles has allele 11 of the first and allele 12 of the second locus.With similar reasoning, it is observed that cluster 7 projects heavilyalong the negative direction of the third principal component. This isinterpreted to be that the members in this cluster would have allele 11of both loci, and the absence of allele 12 in both loci. In fact, 100%of the members are this way. Notice that cluster 5 projects almost tothe dead center of the origin. This is interpreted to be that members inthis cluster either have both alleles or neither allele for each locus,so that the effects of the elements of the principal components for eachlocus cancel. As seen in FIG. 21, this is the case.

EXAMPLE 19 Clustering by k-Means and Differentiation of Clusters

The nine distinct clusters can be established analytically by thek-means cluster algorithm. Clusters identified by k-means were validatedby visual inspection. Memberships within each cluster were analyzed toget at the similarity among the members. FIG. 21 shows a plot of thefraction within each cluster possessing each allele. It is observed thatclusters differ in the combination of alleles at each of the 2 loci thatare dominant (allele 11 and 12 of both loci). For instance, members ofcluster 1 all have the 5th allele of the d13s17 locus (allele 11) andthe 8th allele(17−9=8; d13s17 has 9 alleles) of the d16s539 locus(allele 12). From the make up of the principal components, theprojections of each clusters onto each principal component can bepredicted by looking at the presence or absence of these alleles in themembers of the clusters.

Because the most probable alleles for the d13s locus are alleles 11 and12, and the most probable alleles for d16s539 are alleles 11 and 12 (orindex number 16 and 17 in FIG. 21 below), the clusters correspond toprofiles with various combinations of presence or absence of thesedominant alleles at these four positions. Table 3 shows the combinationsof these four dominant alleles in each of the nine clusters, based onthe plots shown in FIG. 21. The assignment of the allele distribution inthese four dominant alleles in each of these nine clusters as well asthe factor that caused the points to cluster this way is furtherelaborated below.

From Table 3, Boolean expressions can be written that form logical testson the data to determine cluster assignment. For example, a Booleanexpression testing for membership of a DNA profile in cluster 1 is“(d13s17-allele11) and not (d13s17-allele12) and not (d13s539-allele11)and (d13s17-allele12)”, where the terms in parentheses are logicalvariables that are true if the corresponding allele is present and falseotherwise. A more complex example is the Boolean expression testing formembership in cluster 5: “(((d13s17-allele11) and (d13s17-allele12)) ornot ((d13s17-allele11) or (d13s17-allele12))) and (((d16s539-allele11)and (d16s539-allele12)) or not ((d16s539-allele11) or(d16s539-allele12)))”. This expression requires both alleles from eachlocus to be either present or absent in order to be true. Booleanexpressions can be rewritten in various forms and simplified accordingto methods that are well known and practiced in the fields of Booleanalgebra and logic circuit design.

Table 3 can also be utilized to form a decision tree that sequentiallyapplies tests for the presence or absence of alleles at specific lociusing the methods of inductive inference that were pioneered by J. RossQuinlan [13] and are well known and practiced in the fields of computerscience and engineering. In this case, each node of the database treethat utilizes clusters derived from the multivariate statisticalanalysis method would contain a decision tree specifying the sequence oftests to be applied to DNA profile targets at that node, and thedatabase tree can be rewritten by expanding these nodes andincorporating the decision tree's nodes into the database tree.

TABLE 3 The presence (1) or absence (0) of alleles 11 and 12 in the d13sand d16s loci for each scores cluster as shown in FIG. 13. D13s17 D13s17D16s539 D16s539 Cluster allele 11 allele 12 allele 11 allele 12 1 Yes(1) No (0) No (0) Yes (1) 2 Yes (1) Yes (1) No (0) Yes (1) 2 No (0) No(0) No (0) Yes (1) 3 No (0) Yes (1) No (0) Yes (1) 4 Yes (1) No (0) No(0) No (0) 4 Yes (1) No (0) Yes (1) Yes (1) 5 Yes (1) Yes (1) Yes (1)Yes (1) 5 Yes (1) Yes (1) No (0) No (0) 5 No (0) No (0) Yes (1) Yes (1)5 No (0) No (0) No (0) No (0) 6 No (0) Yes (1) No (0) No (0) 6 No (0)Yes (1) Yes (1) Yes (1) 7 Yes (1) No (0) Yes (1) No (0) 8 No (0) No (6)Yes (1) No (0) 8 Yes (1) Yes (1) Yes (1) No (0) 9 No (0) Yes (1) Yes (1)No (0)

EXAMPLE 20 What Makes the Points Cluster

The consequence of having the allele probability densities concentratedin just a few alleles of a locus is now analyzed. As presentedpreviously, the SVD of a matrix decomposes a data set into its mutuallyorthogonal components arranged in decreasing order of the amount ofvariance carried. Each scores vector is obtained by multiplying each DNAprofile (a row of the data matrix, X) by the columns of the V matrix ofEq. 1 above. The columns of V are the principal component vectors. Theith element of a scores vector represents the inner product of thatprofile with the ith column of V. Table 4 shows the make up of the V2and V3 vectors (the second and third principal components). Note thatthe 5th and 6th (allele 11 and 12 of d13s17) as well as the 16th and17th (allele 11 and 12 of d16s539) components in each vector aredominant (but have opposite signs with each other) with the highestabsolute values among all the elements. The significance of this wasexplained in the previous sections.

During the projection step, the inner product of a row of the DNAprofile matrix with each of these V column vectors is formed to producethe scores vector associated with that DNA profile. Recall that a row ofthe DNA profile consists of 1's and 0's, with a 1 indicating thepresence of that allele whose position the 1 occupies. Therefore, informing the inner product, if a 1 is present at the 6th and 16thpositions (corresponding to allele 12 of the d13s locus and allele 11 ofthe d16s locus) and a 0 is present at the 5th and the 17th positions(corresponding to the absence of allele 11 and allele 12 of d13s andd16s respectively), then the resultant inner product is going to be thehighest in the positive sense, of all possible allele presence/absencepattern. The other elements of the V vector are insignificant becausetheir magnitudes are significantly smaller than these four dominantones. In contrast, if the opposite is true in that the patterns of 1'sand 0's are reversed in these four alleles, then a score with thehighest value in the negative sense will result. If a 1 is present inonly one of the four dominant alleles then an intermediate number willbe formed upon taking the inner product. The inner product with V2 givesthe projection onto the 2nd principal component, and thus the x-axiscoordinate in the 2-d scores plot. The inner product with V3 gives theprojection onto the 3rd principal component, and thus the y-axiscoordinate in the same plot. Therefore, all profiles with a similardistribution of 1's and 0's among these four dominant allele positionswill be projected close to each other, forming a cluster. Profiles with1's present in only one of the four dominant alleles will be projectedinto separate and distinct groups intermediate between the two extremeclusters. Profiles with 0's present at all four of these dominant allelepositions will project into a cluster close to the origin.

The non-dominant components of V2 and V3 contribute “noise” that causesdiversity among the points in each cluster. Cluster assignment isdetermined by the dominant components. These dominant componentscorrespond to specific alleles whose presence or absence determinecluster membership. A manual or automated procedure can be utilized todetermine which loci pairs will exhibit good clusters. The preferredpairs of loci are those that have few dominant components in V2 and V3.A second discovery is that the PCA method tends to produce clusters ofroughly equal size. This is a consequence of the relative magnitudes ofthe probability densities over the alleles at each locus and thegrouping of patterns of the alleles that correspond to the dominantcomponents. The PCA method tends to produce groupings of allele patternsthat result in clusters of roughly equal size. This property isimportant because it leads to the generation of balanced database trees,and thus tends to minimize average and worst-case search times.

TABLE 4 The second (V2) and third (V3) principal components of the PCAmodel for d13s17 and d16s539 profiles. Entry Locus Allele V2 V3 1 d13s17 7 0 0 2 d13s17  8 −0.030463678 0.015674884 3 d13s17  9 0.014273974−0.02612085 4 d13s17 10 0.002730887 0.006265153 5 d13s17 11 −0.60159885−0.31240033 6 d13s17 12 0.61424995 0.40528092 7 d13s17 13 0.01506822−0.027059415 8 d13s17 14 0.008189252 −0.002920761 9 d13s17 15 0.000400020.00072532 10 d16s539  5 −3.96254E-16 1.24058E-16 11 d16s539  6−1.21109E-16 3.920160E-17 12 d16s539  7 3.16893E-16 −1.01547E-16 13d16s539  8 0.00016862 −0.005327745 14 d16s539  9 −0.0090213590.035054583 15 d16s539 10 0.010145883 0.009643393 16 d16s539 110.36385758 −0.61572765 17 d16s539 12 −0.3559744 0.59259863 18 d16s539 13−0.006374183 0.068562668 19 d16s539 14 0.002505632 0.009997444 20d16s539 15 −0.000962222 0.000468172 Scaling facor 0.0152088020.015862441

The center of each cluster is the center of gravity of the swarm ofpoints in that cluster. Table 5 shows where the centers are with respectto the 2-d scores plot of FIG. 13. Based on the above rationale forformation of the clusters, the approximate centers of the nine clustersas observed in the scores plot of FIG. 13 can be predicted from the setof all possible 1's and 0's distribution among the four dominant allelepositions. The prediction can be checked against the true centers of theclusters. This is explained in the following section.

TABLE 5 The coordinates of the centers of the nine clusters shown inFIG. 13. Cluster Centers x y −0.0168 0.0054 −0.0062 0.0116 0.0045 0.0179−0.0105 −0.0052 0.0001 0.0011 0.0108 0.0074 −0.0042 −0.0159 0.0064−0.0096 0.0171 −0.0033

EXAMPLE 21 Testing the Theory of Clustering by Predicting theApproximate Centers of the Clusters

Table 6 shows all the possible 1-0 distribution patterns at the fourdominant allele positions. The approximate predicted x and y coordinatesfor the cluster centers are calculated by multiplying the corresponding1's and 0's at the four dominant allele positions with their counterpartvalues in the V1 (to get the x coordinate) and V2 (to get the ycoordinate) vectors which were shown in Table 3 previously. This isfollowed by a normalization step in which the previous products aremultiplied by the scaling factors shown at the bottom of Table 5, inorder to arrive at the Mahalanobis scores. These scaling factorscorrespond to the reciprocals of the 2nd and 3rd singular values of theSVD of the original data matrix, X. The predicted approximatecoordinates for the cluster centers are shown at the rightmost twocolumns of Table 5. These points are plotted as the ‘o’ points in FIG.22. The true cluster centers are also plotted in FIG. 22, as the ‘*’points. It is evident that the two sets are very close. All profiles inthe original 10,000 profile set with identical allele distributionpattern in these four dominant allele positions will map to the samecluster. They will differ from each other somewhat, due to the presenceor absence of alleles at other allele positions, which play a minor rolein determining the coordinates of the corresponding profile in the 2-dscores plot. All possible allele distribution patterns in the fourdominant positions fall into a total of nine clusters, as shown in Table3 above. This experiment supports the explanation rendered above inregard to the formation of clusters in the 2-d scores space.

In summary, loci with allele probability densities concentrating at justa few alleles will give rise to V vectors with preferentially big values(in the absolute value sense) at just a few positions corresponding tothe allele positions with high densities. (The fact that both alleles 11and 12 in the d13s and d16s loci are the dominant alleles are just acoincidence.) As a result, distinct clusters will form, separable by thepresence or absence of alleles at these dominant allele positions.

TABLE 6 The presence (1) or absence (0) of alleles at the four dominantallele positions and the approximate cluster center coordinatescalculated from the 1-0 allele distribution pattern. See text forcalculation of these approximate coordinates. d13s17 d16s539 ProjectionsN 11 12 11 12 x y 1 0 0 0 0 0 0 2 0 1 0 0 0.009342 0.006429 3 1 0 0 0−0.00915 −0.00496 4 1 1 0 0 0.000192 0.001473 5 0 0 0 1 −0.00541 0.00946 0 1 0 1 0.003928 0.015829 7 1 0 0 1 −0.01456 0.004445 8 1 1 0 1−0.00522 0.010873 9 0 0 1 0 0.005534 −0.00977 10 0 1 1 0 0.014876−0.00334 11 1 0 1 0 −0.00362 −0.01472 12 1 1 1 0 0.005726 −0.00829 13 00 1 1 0.00012 −0.00037 14 0 1 1 1 0.009462 0.006062 15 1 0 1 1 −0.00903−0.00532 16 1 1 1 1 0.000312 0.001106 Scaling factor: 0.01520880.0158626

EXAMPLE 22 Clustering Analysis of a Real DNA STR Data Set

All the work reported above was done with 10,000 synthetic dataprofiles, generated based on the allele frequency distribution data forCaucasians as given in the CODIS data base. Recently, Budowle [10]released the STR profiles of six ethnic groups, each of which has around200 samples. We tested whether PCA clusters from these data wouldproject to the same clusters as that of the synthetic data, using theprincipal components from the latter to do the projection, and if therelative sizes of the clusters were maintained. Therefore, a smallCaucasian sample data set from one of the six real DNA sample set waschosen for further analysis. This was done to determine whether or notnew data inserted in the database would tend to degrade the balancedstructure of the database tree and thus adversely affect mean and worstcase search times.

The sample set was first converted to the binary representation format,with 1's and 0's. The corresponding allele information in the d13s17 andd16s539 loci was extracted. This was followed by computing the scoresmatrix onto the 2nd and 3rd principal components of the large syntheticdata set. FIGS. 23 and 24 show the results. In FIG. 23, the scorespoints from the large data set are overlaid on top of the scores pointsfrom the small data set. FIG. 24 shows the same thing except in thisplot, the scores from the small sample set are overlaid on top of thoseof the large sample set. The black points depict the scores from thesmall sample set. Since there are only 176 of them, they do notcompletely cover the 10000 score points from the large data set. It isevident that the plotted scores from the small data (which are mapped tothe same 2-d coordinates in a cluster as the scores from the large dataset and are plotted as darker dots) are completely covered by those ofthe large data set (the dark gray points). This was interpreted to meanthat there is no profile present in the small real DNA sample set thatis not present in the large synthetic data set. This complete coverageis not always the case. Studies using other 2-loci combinationssometimes yield incomplete coverage. In all instances studied to date,however, the plotted 2-d coordinates for points in the small datasetswere easily associated with clusters identified using the synthetic dataset.

Next, estimates of the probability densities associated with the variousclusters are derived. We identified which profiles from the small sampledata set are in each of the nine clusters. We then calculated thefraction of the sample population that are within each of the nineclusters. FIG. 25 shows the comparison between the two data sets. Thefirst bar of each pair of bars represents that from the small sampleset, while the second bar denotes that of the large data set. To a firstlevel approximation, the relative fractions are comparable between thetwo sets. The fraction of people in each cluster indicates theapproximate fraction of people possessing the particular combination ofthe alleles at the dominant allele positions, thus the relativefrequency of the occurrence of those dominant alleles in the associatedlocus. Note that the trend of the variation of the height of the barsfor both sets are similar, except for that of the last cluster. It wasconcluded at this point that the two data sets have similar allelefrequency distribution at these two loci. An important observation isthe relative frequencies indicate that the sizes of the clusters arebalanced in both data sets. This implies that addition of the data fromBudowle to a database containing the synthetic data will not cause adatabase tree that utilizes clustering for the (d13s17, d16s539) locuspair to be unbalanced. Thus, search times will not be adverselyaffected.

EXAMPLE 23 Summary for the Clusterability of Profiles by the PrincipalComponent Analysis Approach

This method can be extended by either (a) using more than two loci or(b) using more than two principal components (or both) to form clusters.It is possible, however, to utilize too much information, in which caseclustering will not be achieved. For example, the use of PCA methods toanalyze allele information for 16 loci simultaneously does not exhibitclustering. Thus, an important discovery of the inventors is that it isadvantageous to limit the application of PCA methods to a portion of theavailable information to achieve good clustering results. In the workillustrated here, the information was limited to allele data for twoloci. In this case, 40 out of 120 possible two-loci combinationsexhibited good clustering properties, as listed in Table 3.

It is firmly established that DNA STR profiles can be partitioned intodistinct clusters using the PCA approach. The partition is based on theallele distribution pattern at 2 loci. Certain 2-loci choices yield muchbetter clustering than others. The factors that determine goodclustering and the reason for the clustering have been presented anddiscussed. Successive partitioning using a different 2-loci combinationapproach at each round will reduce very quickly the members presentwithin each resultant cluster. Partitioning by PCA clustering can beinserted into a suitably chosen non-terminal Node object of the databasetree structure, for searching for matching profiles against a targetprofile. After passing through this node, it is expected that the numberof candidate profiles for search will be reduced by approximately oneorder of magnitude. (Seven to nine clusters usually result from PCAclustering in which the clusters are about equal in size.)

EXAMPLE 24 Performance Analysis

The existing FBI CODIS database search engine requires approximately 5seconds to search 100,000 DNA profile records for matches. Incomparison, a database of synthetic DNA profile data was created usingthe statistical information provided with the FBI CODIS database. Thisdatabase contained 400,000 DNA profiles and required a database treewith 13 levels and 13,180 Node objects. The memory required to store thetree was 218 Mbytes. The time required to load the database from anASCII file that contained descriptions of the DNA profiles was 19minutes 22 seconds. Search times for the test cases that have been runto date on the 400,000 profile database range from 1,200 microseconds to4,200 microseconds, an improvement of greater than a factor of 1,000over the CODIS implementation. These times are for searches for exactmatches.

Additional tests were made using a database of 100,000 DNA profiles. Foreach test a DNA profile was randomly selected from the database and usedto construct a search request. Exact matches were required. Over 5,019runs the mean time required to complete a search and retrieve thematching sample(s) was 2,132.6 microseconds. Of the 5,019 runs, 98.5% ofthe searches completed in less than 5,000 microseconds. A histogramshowing the distribution of times required to perform the search isshown in FIG. 26.

When locus data are missing the search times increase. For a singletarget DNA profile match times increased from approximately 1,700microseconds to 4,200 microseconds on a database of 10,000 samples. Whenmatching is allowed on all but a single locus search times increased byapproximately an order of magnitude to 17,000 microseconds. Tests werealso conducted when equivalent alleles were defined, but are notdirectly comparable to the base case because a database of 1,000 DNAprofiles was used. Searches required approximately 2,300 microseconds.

The method of database construction maintains a well-balanced databasetree. FIG. 27 shows the graph of the tree for a database holding 100,000DNA profiles with a maximum of 100 profiles stored at any leaf node. Thetree has a maximum depth of 11 (levels 0 through 10) with most brancheshaving a length of 7 to 9. Similar results have been obtained for400,000 stored DNA profiles where the tree's maximum depth was 13. Thebalanced characteristic of the tree is important because it determinesaverage and worst case search time. If the tree becomes unbalanced thena substantial fraction of Search Requests will require the descent ofrelatively long branches and will therefore require additional time.

EXAMPLE 25 Parallel Database Design and Hardware Platform

The database tree methods described above can also be implemented inparallel or by using multi-threaded software. The parallelimplementation executes Search Engine and Search Queue objects on eachhost, with at least as many Search Engine objects as there areprocessors on a host. A root host is used to accept Client requests andcreate a Search Client object to handle each request. A criticalcomponent of the parallel implementation is the method used to balancethe work load across the set of available hosts and processors. Thismethod is distributed; it must run on each host. The method alsoresponds to changing load patterns on the hosts, giving faster hostsmore work. The method is reconfigured in the event of host failure(s).

Preferably, each host is allowed to maintain information on thepopulation of available parallel virtual machine (PVM) hosts,measurements of their current loads (Search Queue lengths), andmeasurements of their capacities. Each host is responsible for gatheringits statistics and broadcasting this information to the otherparticipating hosts. As Search Queues become unbalanced, unprocessedSearch Requests are exchanged to bring them back into balance. Thisexchange occurs randomly with a stochastic selection method utilized todetermine the recipient of each exchange. In this manner control of theload balancing method equates to control of the probabilities of hostselection. These probabilities are preferably proportional to thedifference between that host's capacity and its load, weighted by thetotal of these differences over all hosts. A time constant is utilizedto avoid excessive oscillations in host loading.

Two hardware platform options can be employed as hosts for the paralleldatabase implementation. One utilizes generic PC hardware operatingunder the Linux operating system; the other utilizes a Sun MicrosystemsHPC 10000 server and the Solaris operating system. Both utilize theParallel Virtual Machine (PVM) software [7] package to coordinateinterprocess communications and synchronization. An asynchronoustransfer mode (ATM) interconnect can also be used for the generic PCimplementation, utilizing OC-12c (622 Mbps) connections betweenequipment racks and OC-3c (155 Mbps) connections within each rack. Theconfiguration is scalable from 8 to 128 processors, with two additionalcontrol processors, in increments of 16 processors. A Control Rackhouses a high performance ATM switch, such as the Fore Systems ASX-1200,configured in a star topology with OC-12c links to the PC Racks. Thecontrol rack also houses the control processors, a tape backupsubsystem, a video and keyboard switch, and dual uninterruptable powersupplies (UPS). A variation of this implementation is to replace the ATMinterconnect with Fast Ethernet, Gigabit Ethernet, or another networkingapproach. Combinations of networking approaches may be used. Performanceis dependent on the approach. The approach described is preferred.

The generic PC implementation can contain from one to eight PC racks,each housing eight rack-mounted dual processor PCs, a midrange ATMswitch such as the Fore Systems ASX-200, and an UPS. All PC processorsare specified as 500 MHz Pentium IIIs, although it is preferable to usethe fastest chipset available at the time for construction of thesystem. Each PC is configured with 512 MB to 1 GB of RAM, and 54 GB ofhard disk space. Performance figures of merit for the system include 3.5TB (terabytes) of disk storage, 64-128 GB aggregate memory, maximumsustained aggregate interprocessor bandwidth of 10 Gbps (non-blocking),with a maximum per PC bandwidth of 155 Mbps and rack-to-rack of 1.24Gbps (non-blocking in each case). The estimated peak floating pointperformance on the linpack parallel benchmark is 40-60 Gflops, with anestimated peak aggregate instruction rate of 64 GIPS, assuming 500 MHzprocessors. This implementation strategy is similar to the Linux Beowulfsupercomputer clusters pioneered by NASA [8].

The Sun HPC 10000 server scales from 4 to 64 400 MHz SPARC processors,with a configuration of 4 processors per board and a maximum of 16boards. Input/output subsystems are attached to each processor board,and a 102 Gbps processor interconnect is utilized with 10 Gbps bandwidthto memory. In excess of 60 TB of disk space can be configured. The HPC10000 supports clustering with up to 4 HPC 10000's in a cluster. Theplatform supports both PVM and MPI. Linpack-parallel benchmark resultsfor a 64 processor Sun HPC 10000 have been reported at 43.8 Gflops. Sunclaims a peak instruction rate of 100 GIPS.

Each configuration has its merits and disadvantages; however, eitherconfiguration can achieve the necessary performance for the nationalCODIS database. The Sun solution is probably substantially more costly;however, Sun offers maintenance and support contracts. A potentialdisadvantage of the Sun configuration is the shared memory architecturewith a 10 Gbps memory bandwidth limitation. The fully distributedgeneric PC implementation provides local memory for each processor;however, a disadvantage is setup latency across the ATM switches andcontention for the 10 Gbps non-blocking bandwidth of the Fore ASX-1200switch. A substantial long-term advantage of the generic PC solution isthat processors can be readily swapped out at will and upgraded withnewer technology. Replacement of failed units with spare rack-mountablecomputers is also easy, allowing repair as time and resources permit.The generic PC solution has the advantage of being able to track thecontinuing evolution of processor performance, which has historicallyprovided a rough doubling of performance every 18 months. It is unclearwhether similar upgrade paths will be available for the Sun HPC 10000architecture.

Referring to FIG. 28, there is shown an example of a fully developedparallel architecture implementation of the present invention. Panels2800-1 to 2800-M are fully modular and can grow in increments of onepanel to a full complement of M panels (eight of which are shown).Moreover, each panel 2800 comprises two or more processors 2820 suchthat a first panel may be built and additional panels added in stages asthe architecture grows to meet increasing database size and trafficdemand of queries and retrievals of the database. Panel 2801 is acontrol panel and provides control operations for panels 2800-1 to2800-M using one or more control hosts 2811. Central module 2810-1 to2810-M of each panel comprises a bus control module providing datalinking capabilities and bus control for coupling computer hosts on itspanel to bus control module 2821 on control panel 2801 and through buscontrol module 2821 to control hosts 2811 and all other computer hosts2820 of panels 2800-1 to 2800-M. Each panel 2800-1 to 2800-M comprises Nprocessors 2820-1 to 2820-N.

References

1. Cormen, Thomas H., Charles E. Leiserson, and Ronald L. Rivest,Introduction to Algorithms, MIT Press (Cambridge, Mass.)/McGraw-Hill(New York). 1990.

2. Guttman, A., R trees: a dynamic index structure for spatialsearching, ACM, 1984, 47-57.

3. Sellis, T., et. al., The R*-tree: a dynamic index formulti-dimensional objects, Tech. Rept. UMI-ACS TR 87 3, CS TR 1975,University of Maryland, Feb. 1987, 1-24.

4. Agrawal, R. and J. C. Shafer, Method and system for performingproximity joins on high-dimensional data points in parallel, U.S. Pat.No. 5,884,320, Mar. 16, 1999.

5. Message Passing Interface Forum, MPI: A Message-Passing InterfaceStandard, version 1.1, June, 1995. Also athttp://www-unix.mcs.anl.gov/mpi/.

6. Universal Data Option for Informix Dynamic Server, version 9.14 forWindows NT and UNIX. Also athttp://www.informix.com/informix/techbriefs/udo/udo.pdf.

7. Geist, A., A. Begnelin, J. Dongarra, W. Jiang, R. Manchek, V.Sunderam, PVM: Parallel Virtual Machine: A Users' Guide and Tutorial forNetworked Parallel Computing. MIT Press. 1994.

8. Beowulf Project at CESDIR,http://cesdis1.gsfc.nasa.gov/linux/beowulf/, Center of Excellence inSpace Data and Information Sciences, NASA Goddard Space Flight Center.1998.

9. Strang, G., Linear Algebra and its Applications, 2nd ed., AcademicPress, New York, 1980.

10. Budowle, Bruce and Tamyra R. Moretti, “Genotype profiles for sixpopulation groups at the 13 CODIS short tandem repeat core loci andother PCR based loci,” Forensic Science Communications, FBI LaboratoryDivision Publication 99-06, U.S. Department of Justice, Federal Bureauof Investigation. July 1999, V. 1, n. 2.

11. CODIS 5.1 GDIS Searching Specification (Draft), U.S. Department ofJustice Federal Bureau of Investigation. Jul. 23, 1998.

12. Tou, Julius T. and Rafael G. Gonzalez, Pattern RecognitionPrinciples, Addison-Wesley, Reading, Mass., 1992.

13. Quinlan, J. R., Induction of decision trees, Machine Learning1:81-106, 1986.

14. Berry, Michael W., Zlatko Drmac, and Elizabeth R. Jessup, “Matrices,vector spaces, and information retrieval,” SIAM Review 41:335-362, 1999.

We claim:
 1. A method of organizing the data records of a database intoclusters, comprising the steps of: (a) representing one or morevariables in each data record in a binary form, whereby the value ofeach bit is assigned based on the value of a variable; (b) choosing aset of variables from those represented in all of the data records,whereby principal component analysis of the set of variables yieldsdistinct clusters of the data records; (c) applying principal componentanalysis to the chosen set of variables for a sample of the datarecords, whereby two or more principal component vectors are identified;(d) examining the scores of the chosen set of variables for the sampledata records along the two or more vectors; (e) selecting those vectorsof the two or more vectors for which the examined scores form distinctclusters; (f) formulating a test based on the selected vectors; and (g)performing the test formulated in step (f) on each data record, wherebythe data records are organized into clusters.
 2. The method of claim 1,wherein the value of each bit is assigned in step (a) based on whetherthe value of a variable is within a designated range of values.
 3. Themethod of claim 1, wherein the value of each bit is assigned in step (a)based on whether a designated value of a variable is present.
 4. Themethod of claim 1, wherein steps (c) through (e) are performed on asample of data records from a different database.
 5. The method of claim1, wherein the test formulated in step (f) comprises: projecting thechosen set of variables from a data record onto the selected principalcomponent vectors; scaling the projected values; calculating a distancefrom the vector of scaled projected values to a representative samplevector of each cluster; and assigning the data record to the clustersassociated with the least distance.
 6. The method of claim 5, whereinthe representative sample vector of each cluster is the cluster center.7. The method of claim 1, wherein the data comprise DNA profiles.
 8. Themethod of claim 7, wherein the represented variables are alleles at twoor more polymorphic loci.
 9. The method of claim 8, wherein the value ofeach bit is assigned in step (a) based on whether a designated allele ispresent.
 10. The method of claim 1, wherein the chosen set of variablesand selected principal component vectors yield distinct clusters ofapproximately equal size.
 11. The method of claim 1, wherein each testdefines a partition of data of the database according to one ofentropy/adjacency partition assignment or data clustering usingmultivariate statistical analysis.
 12. The method of claim 1 wherein thevariable in step (a) comprises more than one value, wherein each of themore than one value is represented in binary form.
 13. The method ofclaim 1 wherein the binary form representation of step (a) is providedas a matrix, said matrix comprising a row for each data record and acolumn for each value of each variable, and wherein the row for eachdata record contains binary encoded information regarding the presenceor absence of the value of each variable in the data record.
 14. Amethod of organizing the data records of a database into clusters,comprising the steps of: (a) representing one or more variables in eachdata record in a binary form, whereby the value of each bit is assignedbased on the value of a variable; (b) choosing a set of variables fromthose represented in all of the data records, whereby multivariatestatistical analysis of the set of variables yields distinct clusters ofthe data records; (c) applying multivariate statistical analysis to thechosen set of variables of a sample of the data records, whereby two ormore vectors are identified; (d) examining the vectors of inner productsof the chosen set of variables of the sample data records with theidentified vectors; (e) selecting the identified vectors that cause thevectors of inner products to form clusters; (f) formulating a test basedon the selected vectors which assigns each data record to a cluster; and(g) performing the test formulated in step (f) on each data record,whereby the data records are organized into clusters.
 15. The method ofclaim 14, wherein the value of each bit is assigned in step (a) based onwhether the value of a variable is within a designated range of values.16. The method of claim 14, wherein the value of each bit is assigned instep (a) based on whether a designated value of a variable is present.17. The method of claim 14, wherein steps (c) through (e) are performedon a sample of data records from a different database.
 18. The method ofclaim 14, wherein the test formulated in step comprises: projecting thechosen set of variables from a data record onto the selected vectors;scaling the projected values; calculating a distance from the vector ofscaled projected values to a representative sample vector of eachcluster; and assigning the data record to the clusters associated withthe least distance.
 19. The method of claim 18, wherein therepresentative sample vector of each cluster is the cluster center. 20.The method of claim 14, wherein the data comprise DNA profiles.
 21. Themethod of claim 20, wherein the represented variables are alleles at twoor more polymorphic loci.
 22. The method of claim 21, wherein the valueof each bit is assigned in step (a) based on whether a designated alleleis present.
 23. The method of claim 14, wherein the chosen set ofvariables and selected vectors yield distinct clusters of approximatelyequal size.
 24. The method of claim 14 wherein the variable in step (a)comprises more than one value, wherein each of the more than one valueis represented in binary form.
 25. The method of claim 14 wherein thebinary form representation of step (a) is provided as a matrix, saidmatrix comprising a row for each data record and a column for each valueof each variable, and wherein the row for each data record containsbinary encoded information regarding the presence or absence of thevalue of each variable in the data record.
 26. A method of organizingdata records of a database into clusters, comprising: (a) choosing a setof variables to represent data records of a database and a set ofprincipal component vectors with which the set of variables cluster thedata records, said step of choosing being performed by: (i) selectingone or more variables present in all of the data records; (ii) applyingprincipal component analysis to the selected variables of a sample ofthe data records to obtain principal component vectors; (iii) projectingthe selected variables of a sample of the data records onto theprincipal component vectors; (iv) determining whether the projections ofthe selected variables of the sample of data records form clusters; (v)repeating steps (i)-(iv) to obtain the set of variables and the set ofprincipal component vectors that form clusters; (b) projecting thevariables chosen in step (a) for the data records onto the principalcomponent vectors chosen in step (a); (c) formulating a test whichassigns each data record to a cluster, wherein the test is based on thevariables chosen in step (a); (d) performing the test on each datarecord, whereby the data records are organized into clusters.
 27. Themethod of claim 26, wherein the principal component vectors chosen instep (a) contain two vectors.
 28. The method of claim 26 wherein thevariables selected in step (a) are represented in binary form based onwhether the values of the variables are within a designated range ofvalues.
 29. The method of claim 26 wherein the variables chosen in step(a) are represented in binary form, and wherein the value of thevariable is assigned based on whether a designated value is present. 30.The method of claim 26 wherein steps (a)(ii)-(a)(iv) are performed ondata records from a different database.
 31. The method of claim 26wherein the test comprises: projecting a first data record onto theprincipal component vectors chosen in step (a); optionally scaling theprojected first data record; calculating a distance between theprojected first data record and at least one representative vector oftwo or more clusters; and assigning the first data record to theclusters associated with the least distance.
 32. The method of claim 31wherein each representative vector is a cluster center.
 33. The methodof claim 26 wherein the data records comprise DNA profiles.
 34. Themethod of claim 33 wherein the set of variables chosen in step (a)comprise allele values.
 35. The method of claim 34 wherein the allelevalues are represented based on whether a designated allele is present.36. The method of claim 26 wherein the set of variables and the set ofprincipal component vectors chosen in step (a) yield distinct clustersof approximately equal size.
 37. The method of claim 26 wherein the testdefines a partition of the data records of the database according toentropy/adjacency partition assignment or data clustering usingmultivariate statistical analysis.
 38. A method of organizing datarecords of a database into clusters, comprising: (a) choosing a set ofvariables to represent data records of a database and a set of vectorswith which the set of variables cluster the data records, said step ofchoosing being performed by: (i) selecting one or more variables presentin all of the data records; (ii) applying multivariate statisticalanalysis to the selected variables of a sample of the data records toidentify two or more vectors; (iii) determining if vectors of innerproducts of the selected variables of the data records and the two ormore identified vectors in (ii) form clusters; (iv) repeating steps(i)-(iii) to obtain the set of variables and the two or more identifiedvectors that form clusters; (b) representing the set of variablesselected in step (a) in binary form, wherein the value of each bit isassigned based on the value of the variable; (c) calculating vectorsinner products between the selected variables of the data records andthe two or more vectors chosen in step (a); (d) formulating a test whichassigns each data record to a cluster, wherein the test is based on thevariables chosen in step (a); (e) performing the test on each datarecord, whereby the data records are organized into clusters.
 39. Themethod of claim 38 wherein the vectors identified in step (a)(iii) aretwo in number.
 40. The method of claim 38 wherein the set of variableschosen in step (a) is represented in binary form based on whether thevalue of the variable is within a designated range of values.
 41. Themethod of claim 38 wherein the set of variables is represented in binaryform based on whether a designated value of a variable is present. 42.The method of claim 38 wherein steps (a)(ii)-(a)(iii) are performed ondata records from a different database.
 43. The method of claim 38wherein the test comprises: projecting a first data record onto thevectors chosen in step (a); optionally scaling the projected first datarecord; calculating a distance between the projected first data recordand at least one representative vector of two or more clusters;assigning the first data record to the clusters associated with theleast distance.
 44. The method of claim 43 wherein each representativevector is a cluster center.
 45. The method of claim 38 wherein the datarecords comprise DNA profiles.
 46. The method of claim 45 wherein theset of variables chosen in step (a) comprise allele values.
 47. Themethod of claim 46 wherein each value indicates whether a designatedallele is present.
 48. The method of claim 38 wherein the set ofvariables and the set of vectors chosen in step (a) yield distinctclusters of approximately equal size.
 49. The method of claim 26 whereinthe test defines a partition of the data records of the databaseaccording to entropy/adjacency partition assignment or data clusteringusing multivariate statistical analysis.